Method and apparatus for short-term prediction of convective weather

ABSTRACT

A method and apparatus for forecasting the likely occurrence of convective weather events, such as thunderstorms. An image filter is used to identify areas of interest within a meteorological image that are likely to contain convective weather. The image filter and an image difference processor identify areas within the meteorological image that are likely to experience a growth and/or decay of weather events. The meteorological image, interest image and growth/decay image are processed to produce the short-term forecast.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefits of U.S. ProvisionalApplication Serial No. 60/269,995 filed on Feb. 20, 2001.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] The subject matter described herein was supported in part underContract Number F19628-00-C-0002 awarded by the U.S. Department of theAir Force.

FIELD OF THE INVENTION

[0003] The invention relates generally to weather image processing andmore specifically to image processors that generate short-termpredictions of convective weather based on determination of the growthand decay of convective weather events.

BACKGROUND OF THE INVENTION

[0004] Short-term weather predictions (e.g., 10-120 minutes) of thelocation and severity of storms are extremely important to many sectorsof the population. For example, aviation systems, traffic informationsystems, power companies and commuters realize important safety andeconomic benefits from accurate predictions of storms. Short-termforecasts are particularly important for convective storms, such asthunderstorms, in which individual cells can exhibit a lifecycle lessthan the short-term forecast period. The challenge for any short-termforecaster is generating a forecast that is both accurate and reliable.

[0005] Some methods of generating short-term convective weatherforecasts are partially automated, relying on operator invention. Theseapproaches can offer acceptable predictabilities, however, can requiresignificant operator interaction. As with any application relying onoperator intervention, there is a possibility that human error canresult in inaccurate forecasts.

[0006] Other methods of generating short-term convective weatherforecast require little or no operator intervention. Unfortunately, theaccuracy and reliability of these systems is generally insufficient formany applications. Fully-automated systems often “over predict” severeweather events. Such forecasts can exaggerate storm intensity andspatial extent. For applications, such as air traffic control, an overprediction can result in rerouting air traffic unnecessarily, resultingin undesirable inefficiencies, including longer flight times andadditional fuel consumption.

SUMMARY OF THE INVENTION

[0007] In general, the present invention relates to an automated weatherforecaster processing meteorological images from remotely sensed weatherindicators to estimate the short-term growth and decay of convectivemeteorological events, such as thunderstorms. The present inventionovercomes many of the disadvantages of prior art systems by providing afully-automated system that provides substantial improvements inaccuracy and reliability.

[0008] Accordingly, in a first aspect, the invention relates to aprocess for a computer-assisted prediction of near-term development ofconvective meteorological events including the steps of determining adifference image by advecting a first meteorological image and combiningthe advected first meteorological image and a second meteorologicalimage. The first and second meteorological images include dataindicative of a first forecast parameter at a first time and a secondtime, respectively. The process further includes the steps of generatingan interest image including a region of interest by filtering a thirdmeteorological image and generating a growth image indicative of theoccurrence of a convective meteorological event by combining thedifference image and the interest image. The term growth as used hereinindicates both positive growth and negative growth (i.e., decay).

[0009] In one embodiment the step of determining the difference imageincludes subtracting the advected first meteorological image from thesecond meteorological image. In another embodiment the difference imageis determined by averaging a plurality of preliminary difference images.In yet another embodiment the step of generating the interest imageincludes filtering the third meteorological image to generate alarge-scale-feature image and a small-scale-feature image.

[0010] In one embodiment the step of filtering the large-scale-featureimage comprises low-pass filtering the third meteorological image. Inanother embodiment the step of low-pass filtering comprisesneighborhood-average filtering. In another embodiment the step offiltering the small-scale-feature image comprises high-pass filteringthe third meteorological image. In another embodiment the step ofhigh-pass filtering comprises neighborhood-standard-deviation filtering.

[0011] In one embodiment the step of generating the interest imagefurther includes generating a peakiness image identifying cloud peaksindicative of convective weather. In another embodiment the step ofgenerating the peakiness image includes the steps of subtracting thelarge-scale, or average, meteorological image from the thirdmeteorological image.

[0012] In one embodiment, generating a forecast includes the additionalsteps of combining the growth image and the first meteorological imageto generate a forecast image identifying the likelihood of convectivemeteorological events at a third time and advecting the combined imageto the third time. In another embodiment, generating a forecast includesthe additional step of classifying weather elements of the firstmeteorological image, the classifications can be one or more from thegroup including lines, stratiform regions, large cells, and small cells.

[0013] In another aspect, the invention relates to a process for acomputer-assisted prediction of near-term development of convectivemeteorological events including the steps of determining a differenceimage by advecting a first precipitation image, and combining theadvected first precipitation image and a second precipitation image, thefirst and second precipitation images indicative of precipitation at afirst time and a second time, respectively; generating an interest imageincluding a region of interest by filtering the second precipitationimage; and generating a growth image indicative of the occurrence of aconvective meteorological event by combining the difference image withthe interest image.

[0014] In one embodiment the step of combining the advected firstprecipitation image and the second precipitation image includessubtracting the manipulated first precipitation image from the secondprecipitation image. In another embodiment the precipitation imageincludes data representative of vertically integrated liquid (VIL) watercontent. In another embodiment the step of generating the interest imageincludes filtering the first precipitation image to generate alarge-scale-feature image; and filtering the first precipitation imageto generate a small-scale-feature image. In another embodiment the stepof filtering the large-scale-feature image comprises low-pass filteringthe first precipitation image. In another embodiment the step offiltering the small-scale-feature image comprises high-pass filteringthe first precipitation image. Another embodiment includes theadditional steps of generating a forecast image identifying thelikelihood of convective meteorological events at a third time bycombining the growth image and a current precipitation image; andadvecting the combined image to the third time.

[0015] In one embodiment, generating a forecast includes the additionalsteps of advecting a growth image according to a first advection field;advecting a current precipitation image according to a second advectionfield, and combining the advected growth image and advected currentprecipitation image to generate a forecast image.

[0016] In another aspect, the invention relates to a process for acomputer-assisted prediction of near-term development of convectivemeteorological events including the steps of determining a differenceimage by advecting a first infrared meteorological image and combiningthe advected first infrared image and a second infrared meteorologicalimage, the first and second infrared meteorological images areindicative of cloud temperature at a first time and a second time,respectively, generating an interest image including a region ofinterest by filtering a satellite visible meteorological image, andgenerating a growth image indicative of the occurrence of a convectivemeteorological event by combining the difference image and the interestimage.

[0017] In one embodiment the step of combining the advected firstinfrared meteorological image and the second infrared meteorologicalimage includes subtracting the another embodiment the step of generatingthe interest image includes filtering the satellite visiblemeteorological image to generate a large-scale-feature image andfiltering the satellite visible meteorological image to generate asmall-scale-feature image. In another embodiment the step of filteringthe large-scale-feature image includes low-pass filtering the satellitevisible meterological image. In another embodiment the step of filteringthe small-scale-feature image includes high-pass filtering the satellitevisible meteorological image. In another embodiment, generating aforecast includes the additional step of filtering the satellite visiblemeteorological image to generate a peakiness image indicative of cumulusclouds. In yet another embodiment, generating a forecast includes theadditional step of generating a forecast image identifying thelikelihood of convective meteorological events at a third time bycombining the growth image and a current precipitation image andadvecting the combined image to the third time.

[0018] In yet another aspect, the invention relates to a system forpredicting near-term development of convective meteorological events andincludes means for advecting a first meteorological image and combiningthe advected first meteorological image and a second meteorologicalimage to generate a difference image, the first and secondmeteorological images indicative of a first forecast parameter at afirst time and a second time, respectively, filter means for generatingan interest image including a region of interest by filtering a thirdmeteorological image, and means for combining the difference image andthe interest image to generate a growth image indicative of theoccurrence of a convective meteorological event.

[0019] In one embodiment the filter means includes a large-scale featuredetector means for filtering the third meteorological image to generatea large-scale-feature image and a small-scale feature detector means forfiltering the third meteorological image to generate asmall-scale-feature image. In another embodiment the large-scale featuredetector includes low-pass filter means for generating a low-passfiltered rendition of the third meteorological image and the small-scalefeature detector comprises high-pass filter means for generating ahigh-pass filtered rendition of the third meteorological image. Inanother embodiment, the filter means further comprises a peakinessfeature-detector means for generating a peakiness image indicative ofcumuliform features.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The invention is pointed out with particularity in the appendedclaims. The advantages of the invention may be better understood byreferring to the following description taken in conjunction with theaccompanying drawings in which:

[0021]FIG. 1 is a block diagram depicting a sensing and processingsystem comprising a short-term convective weather predictor according tothe invention;

[0022]FIG. 2 is a block diagram depicting an embodiment of a short-termweather predictor;

[0023]FIG. 3 is a flow diagram generally illustrating an embodiment of aprocess for generating short-term convective weather forecasts;

[0024]FIG. 4 is a flow diagram illustrating in more detail the step ofgenerating a difference image for the process shown in FIG. 3;

[0025]FIG. 5 is a flow diagram illustrating in more detail the step ofgenerating an interest image for the process shown in FIG. 3;

[0026]FIG. 6 is a flow diagram illustrating in more detail the step ofgenerating a short-term weather forecast for the process shown in FIG.3; and

[0027]FIGS. 7A through 7D are exemplary schematic diagrams illustratingthe processing of weather images to generate a short-term convectiveweather prediction according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0028] Short-Term Convective Weather Predictor System Overview

[0029]FIG. 1 depicts a system block diagram of a weather sensing andprediction system 100 including one embodiment of the invention forpredicting the initiation, growth and decay of convective weather, suchas cumulus cloud formations and thunderstorms. The system 100 includes ashort-term storm predictor 102 receiving meteorological data from one ormore external sources and a weather forecast display 110. The weatherforecast display 110 provides an image to an observer or image dataappropriate for processing by additional processes or components (notshown). The external sources can include weather sensing systems, suchas ground-based weather sensors 104 (for example, weather radars,airborne weather sensors, and space-based sensors). Alternatively, theexternal sources can include other computer systems, such as computersforwarding weather image files from the external systems and computersthat provide simulated weather image data. For system configurations inwhich the external sources include satellites 106, the satellite data isgenerally first received by a satellite earth station 108, whichperforms some pre-processing on the received satellite data andtransmits the pre-processed satellite data to the short-term weatherpredictor 102. The received meteorological data is indicative of one ormore weather parameters, such as precipitation rate,vertically-integrated-liquid water content (VIL), temperature (e.g.,infrared temperature), albedo, lightning occurrences, moisture, andwind-speed. The meteorological data can also include numerical modeldata or computer generated data indicative of any of the above-mentionedweather parameters. The meteorological data can be transmitted from theexternal source 104, 106 in any number of formats, and can betransformed at the source 104, 106, or at an intermediate processingelement (e.g., the satellite earth station 108) into other formats, suchas a meteorological image. In some embodiments, the meteorological imageincludes a multi-dimensional array, such as a two-dimensional array, ofimage elements. Generally, the meteorological image elements includepixel values which are quantitative measures of weather forecastparameters. For example, each pixel value can be associated with anumber, such as any binary number, representative of a value of aweather parameter (e.g., precipitation rate). Each pixel is generallyassociated with a predetermined geographical location, or geographicalarea, such that the forecast parameter represented by each pixel isindicative of one or more aspects of the weather at the associatedgeographical location, or is indicative of an average ofspatially-varying weather for the associated geographical area.

[0030] The radar 104 can include a system such as the ASR-9, TerminalDoppler Weather Radar (TDWR) or the Next Generation Weather Radar(NEXRAD). The satellite 106 can include a satellite system such as theGeostationary Operational Environmental Satellite (GOES) or the PolarOperational Environmental Satellite (POES). The radar 104 and satellite106 can transmit more than one form of weather-related data. Forexample, the radar 104 can transmit a first channel of data relating toprecipitation and a second channel of data relating to VIL. Similarly,the satellite 106 can transmit a first channel of data relating toinfrared radiation and a second channel of data relating to albedo.Other entities provide similar weather information (commonly referred toas a “ground feed”) that may include remapped, or compositerepresentations of weather information from one or more sources 104,106. The short-term storm predictor 102 processes received data from oneor more sources 104, 106 and/or ground feed and predicts the initiation,development and decay of convective weather by identifying areas ofgrowth and decay.

[0031] In one embodiment the short-term storm predictor 102 determines ashort-term forecast in response to receiving radar data. In anotherembodiment, the short-term storm predictor 102 determines a short-termforecast in response to receiving satellite data. In another embodiment,the short-term storm predictor 102 determines a short-term forecast inresponse to receiving radar data and satellite data. In yet anotherembodiment, the short-term storm predictor 102 determines a short-termforecast in response to receiving numerical model data.

[0032] In one embodiment, illustrated in FIG. 2, the short-term stormpredictor 102 includes an image receiver processor 202 receivingmeteorological images from one or more external sources 104, 106. Theimage receiver processor 202 includes output ports 203, 203′, 203″, eachtransmitting at least one or more processed meteorological image. Animage difference processor 204 communicates with at least one of theimage receiver processor output ports 203, 203′, 203″ and receives afirst and a second processed meteorological image. The image differenceprocessor 204 generates a difference image representing the differenceof the first and second processed meteorological images and transmitsthe difference image to an image growth-and-decay processor 214. Anumber of image filters, including a large-scale image filter 206, asmall-scale image filter 208, and optionally a “peakiness” filter 210and/or a classification filter 211 communicate with at least one of theimage receiver processor output ports 203, 203′, 203″. Each of the imagefilters 206, 208, 210, 211 receives a processed meteorological image,such as one of the first and the second meteorological image, oralternatively, a third meteorological image. Each of the filters 206,208, 210, 211 individually filters the received processed meteorologicalimage to produce a respective filtered image, that is provided to animage filter processor 212. The image filter processor 212 generates acomposite filtered image, or interest image, based on the receivedfiltered images and transmits the interest image to the imagegrowth-and-decay processor 214. The interest image identifies variousregions of the meteorological image likely to contain convectiveweather.

[0033] The image growth-and-decay processor 214 generates agrowth-and-decay image in response to the difference image and theinterest image. In the illustrated embodiment, the image forecastprocessor 216 communicates with the image growth-and-decay processor214, the image receiver processor 202 and, optionally, theclassification filter 211. The image forecast processor 216 receives aprocessed meteorological image from the image receiver processor 202,the growth-and-decay image from the growth-and-decay processor 214 and,optionally, classification data from the classification filter 211. Theprocessed meteorological image can be a precipitation image includingdata forecast parameters indicative of precipitation rates for an arrayof geographical locations. In general, the processed image can includeany parameter indicative of convective weather, such as any of the abovementioned weather parameters. The image forecast processor 216 generatesa short-term convective weather image for transmission to a weatherdisplay unit or other weather processor modules. The short-termconvective weather forecast image indicates the locations and likelihoodof initiation, growth and/or decay of convective weather for a forecasttime period that can be up to 120 minutes or more.

[0034] In one embodiment, the short-term storm predictor 102, asrepresented by the filters 206, 208, 210, 211 and processors 202, 204,212, 216, is implemented in software. The implementing software can be asingle integrated program or module. Alternatively, the implementingsoftware can include separate programs or modules for one or more of thefilters 206, 208, 210, 211 and processors 202, 204, 212, 214, 216. Inother embodiments, the short-term storm predictor 102 is implemented inhardware, such as electronic filters, or circuitry implementing digitalsignal processing. In yet other embodiments, the short-term stormpredictor 102 is implemented as a combination of software and hardware.

[0035] Generally, the image receiver processor 202 receives from one ormore external sources 104, 106 meteorological image files representingone or more weather parameters over a known geographical region. In someembodiments, the received data is in the form of binary files. Forexample, the binary files can be formatted according to standardgraphical formats, such as JPEG, GIF, TIFF, bitmap, or, alternatively,the binary files can be formatted in a custom format. Typically, theimage receiver processor 202 receives updated meteorological images fromeach external source 104, 106 forming a sequence of imagesrepresentative of weather parameters at different times. Generally, theindividual images represent weather parameter values over substantiallythe same geographical region, but differing from the previous image by auniform time interval, e.g., several minutes or more. The image receiverprocessor 202 optionally reformats each received meteorological imagefrom a native format (e.g., bitmap) to a common format suitable forfurther processing (e.g., TIFF). Alternatively, the image receiverprocessor 202 interpolates and/or extrapolates, as required, thereceived meteorological image files received from one or more of theremote sources 104, 106, for example, to align the pixel values to acommon geographical location, or area. Interpolated or extrapolatedalignment can be necessary for system configurations in whichmeteorological images are received from different remote sources 104,106. The image receiver processor 202 can include memory for temporarilystoring one or more of the received meteorological and/or processedimages, or portions of the same.

[0036] The image difference processor 204 stores at least one of theprocessed meteorological images, such as the first processedmeteorological image indicative of a weather parameter at a first time,as subsequent processed meteorological images are received from theimage receiver processor 202. In one embodiment, the image differenceprocessor 204 calculates a difference image by subtracting a transformedversion of the stored first processed meteorological image from a later(e.g., current) meteorological image. In another embodiment, the imagedifference processor 204 calculates multiple preliminary differenceimages. The preliminary difference images are averaged to obtain thedifference image. The difference image is generally representative of atime-rate-of-change in the processed meteorological image (e.g., a timederivative), which representative of the time-rate-of-change in thecorresponding weather parameter. In one embodiment, the image differenceprocessor 204 determines the difference image by subtracting theprevious, stored processed meteorological image from the currentprocessed meteorological image. As the weather features (e.g., cloudformations) generally moves according to local winds, transformation(e.g., advection) of the first processed meteorological image isperformed prior to determining the difference image. Execution of thetransformation step prior to computing the difference image reducesand/or eliminates simple movement, or translation, of weather featuresfrom introducing a false indication of growth or decay. Advection,generally refers to the process of translating portions, or sub-regionsof the processing image, such as individual pixels, or groups of pixels,according to a transform quantity, such as a vector field indicative ofthe prevailing winds at different locations. The image differenceprocessor 204, having advected the previous processing image then,subtracts the advected processing image from the current processingimage. In some embodiments, the image difference processor 204 repeatsthe difference process as each new image is received in the time seriesof processing image. The subtraction process operates to identify andquantify areas of growth and/or decay of the weather parameterrepresented by the pixel values.

[0037] Similarly, the filters 206, 208, 210, 211 receive a time seriesof processed meteorological images. In one embodiment, the filters 206,208, 210, 211 receive the same time series of processed meteorologicalimages as received by the image difference processor 204. In oneembodiment the image difference processor and the filters 206, 208, 210,211 each receive processed meteorological images relating to satelliteinfrared images. In another embodiment, the image difference processor204 receives a first meteorological image originating from a firstexternal source, such as the satellite 106 as described above, and thefilters 206, 208, 210, 211 receive a second meteorological imageoriginating from a second, or alternative source, such as the radar 104.The first and second meteorological images represent weather within thesame general geographic region. Each of the filters 206, 208, 210, 211receives the processed meteorological image and generates a filteredimage. The filtering process can include various filtering methods, suchas standard image filtering techniques or functional templatecorrelations, or electrical (e.g., video) filtering of the spectralcomponents, temporal components, or amplitude components of the receivedimage.

[0038] Generally, the large-scale filter 206 enhances large-scalefeatures of the processed meteorological image. For example, large scalefeatures can be indicative of weather fronts or organized storms. Thelarge-scale image features can be enhanced, for example, by attenuatingsmall-scale features. In one embodiment, the large-scale filter 206 is alow-pass spatial filter, passing image features having low spatialfrequency components and attenuating, or eliminating, image featureshaving high-spatial-frequency components.

[0039] The small-scale filter 208 enhances small-scale features, ordetails, of the received image. Small scale features can be indicative,for example, of single storm cells, or cumulus formations of limitedgeographic extent. In a manner complementary to that employed by thelarge-scale filter 206, the small-scale features can be enhanced, forexample, by attenuating large-scale features. In one embodiment, thesmall-scale filter 208 is a high-pass spatial filter for passing imagefeatures having high-spatial-frequency components and attenuating, oreliminating, low-spatial-frequency image features.

[0040] The peakiness filter 210 enhances image features indicative oflocal maxima within a sub image. The peakiness image reflects structuraldetails of the received weather image indicating regions likely tocontain cumulus formations. In one embodiment, the peakiness filter 210receives a weather image representing albedo. The peakiness filter 210generates a peakiness image by subtracting an average image from thereceived weather image. The large-scale features, or biases, are thusremoved leaving the peakiness image. The peakiness filter 210 cangenerate the average image locally, or can use the already-generatedaverage image from the large-scale filter 206.

[0041] The classification filter 211 identifies weather patterns, ordetails, of the received image. For example, image features referred toas small or large cell can be indicative of single storm cells, orcumulus formations of limited geographic extent. Image features can befurther differentiated into line image features and stratiform imagefeatures. Line features can be indicative of organized storms, such asthose occurring along a weather front and stratiform features can beindicative of large areas of cloud cover, not necessarily associatedwith convective weather.

[0042] The image filter processor 212 generates a composite, filteredimage based on filtered images provided by the filters 206, 208, 210,211. Generally, the composite, filter image emphasizes geographicalareas indicative of the initiation, growth and/or decay of convectiveweather. Likewise, the composite, filter image de-emphasizes geographicareas not associated with the initiation, growth and/or decay ofconvective weather. The de-emphasis process includes identifying areasthat can include convective weather within an organized storm that doesnot exhibit growth or decay. In one embodiment the composite, filteredimage includes an array of numeric, or scaling values. For example,pixel values in emphasized areas can include increased and pixel valuesnot included in the emphasis areas can be decreased. Alternatively, thecomposite, filtered image can include values of unity for areas ofemphasis and values of zero for areas of de-emphasis, effectivelyforming a mask image, or convective-weather template.

[0043] The image growth-and-decay processor 214 generates agrowth-and-decay image based on the difference image and thecomposite-filtered, or interest image. Generally, the growth-and-decayimage is indicative of sub-regions likely to experience growth and decaywithin a forecast time. As the difference image identifies all areaswhere the monitored weather parameter experienced a growth and decay, itcan over predict the initiation, growth and/or decay of convectiveweather. Thus, the image growth-and-decay processor 214 applies theemphasis and de-emphasis of the interest image to the difference imageto more accurately identify the initiation, growth and/or decay ofconvective weather.

[0044] The image forecast processor 216 generates a short-term forecastimage the processed meteorological image, the growth-and-decay image,and, optionally, the feature classification image. In one embodiment,the image forecast processor 216 identifies areas within the processedmeteorological image likely to experience initiation, growth and/ordecay in response to the growth-and-decay image. The identified areas ofgrowth and/or decay can then be predicted using weather models toidentify a future weather parameter value within the meteorologicalimage. This process is repeated for each region of the image and theresulting image is transformed through advection to a representativeforecast image at the desired forecast time. For example, the localimage feature speed and direction can be applied to pixels orsub-regions of the processed meteorological image to translate (i.e.,vector) its pixels or subregions through a distance, proportional to theforecast time, in the corresponding direction.

[0045] Method Overview

[0046] One embodiment of a process implemented by the short-term stormpredictor 102 is illustrated by the flowchart of FIG. 3. The processoperates upon meteorological images received from one or more externalsources 104, 106 (Step 300). Generally, the images identify weatherforecast parameters indicative of convective weather. The meteorologicalimages are typically comprised of pixels, each pixel including a colorand/or intensity indicative of the value, or range, of the correspondingforecast parameter. For example, a meteorological image indicative ofinfrared temperature can be comprised of a two-dimensional array ofpixels. Each image pixel is assigned a color value from a predeterminedrange of colors. Each color represents a predetermined infraredtemperature, or sub-range of infrared temperatures. The lowest andhighest color values would, for example, correspond to the lowest andhighest anticipated infrared temperatures, respectively.

[0047] The image difference processor 204 generates a difference imageusing a first and second received meteorological image (Step 305). Thedifference image is indicative of a time rate of change in the weatherparameter of the received meteorological image. For example, where themeteorological image represents infrared temperature (e.g., cloudtemperatures), the difference image generated from the infrared imageindicates an increase or decrease in infrared temperature (e.g., a riseor drop in infrared temperature between the two images). Generally, thedifference image is similar in form to the first and secondmeteorological images (e.g., an array of pixels), but the pixel-valuescale of the difference image can be different.

[0048] A growth/decay image is generated from the received differenceimage and a received interest image (Step 310) identifies areas of thereceived meteorological images that are likely to be experiencing agrowth, or situation in which the portrayed weather parameter isindicative of the growth or initiation of convective weather. In a broadsense, the term “growth” can at the same time include both positivegrowth (e.g., cumulus cloud formations increasing in altitude) andnegative growth, or decay (e.g., the dissipation of storm cells or cloudformations). Both positive and negative growth are important indicatorsof forecasted weather. A frontal storm can exhibit growth along itsleading edge as new storm cells form and at the same time exhibit decayalong its trailing edge old storm cells dissipate.

[0049] In an optional step, features in the meteorological image areclassified into one of a number of predefined categories (Step 312).Examples of weather classifications include lines, stratiform regions,large cells, and small cells. Through image-processing techniques, theclassification filter 211 identifies regions in the meteorological imageaccording to the predefined weather categories.

[0050] A short-term weather forecast is generated using the currentmeteorological image, the difference image, the generated growth/decayimage and, optionally, the weather classification image (Step 315). Theforecast image generally indicates regions likely to experience, at theforecast time, weather within a predetermined range of severity. In oneembodiment, the image forecast processor 216 transmits an indication ofsevere weather within a predetermined geographical region. Thetransmitted indication can result in an operator alert of the forecastedweather, such as an audible or visual alarm. For example, when theforecast indicates that, within a sector of airspace being controlled byan air traffic controller, there is a substantial likelihood of severeweather occurring at the forecast time, an alarm can be activated toalert the operator as to the situation.

[0051] In more detail, referring now to FIG. 4, the difference image canbe generated by advecting a first received meteorological image andsubtracting the advected image from a second meteorological image (Step400). The first and second meteorological images are representative of aweather parameter at a first time and a second time, respectively, for acommon geographical regions. For example, the first meteorological imagecan be representative of VIL for a predetermined geographical region ata first (reference) time; whereas, the second meteorological image canbe representative of VIL for the same geographical region at a second(later) time.

[0052] In one embodiment, the step of advecting the first image includestranslating sub-regions of the first image according to an advectionfield. The first image is advected to represent an estimate of the firstmeteorological image at a second time. The advection field includes anarray of vector elements overlaying the geographical area of the firstimage. Each vector element of the advection field is indicative of avelocity (direction and speed) of the forecasted parameter at thelocation of the vector element. Generation of the advected image canthen be accomplished by translating sub-regions of the firstmeteorological image from their sensed locations at the first time toestimated locations at the second time according to the advection fieldvector elements. The direction of each sub-region translation isdetermined from the direction represented by the advection vectorelement associated with the sub-region. The distance of the translationof each sub-region is determined from the magnitude (i.e., speed)represented by the advection vector element by first multiplying thespeed by the time difference measured by subtracting the second timefrom the first time.

[0053] In one embodiment, the advection field is generated by trackingthe movement of identifiable features over successive meteorologicalimages. In one embodiment, the advection field is updated with thereception of each new updated meteorological image.

[0054] A second meteorological image is received at a second time (step405). A difference image is generated by subtracting the advected image,representative of the first meteorological image at the second time,from the received second image (step 415). For instances in which thereis little or no change in the weather parameter, the resultingdifference image exhibits little or no change. For example, when aregion of the advected first image is substantially equivalent to thecorresponding region of the second meteorological image, the pixelvalues for those regions in the difference image are approximately zero.Conversely, when new storm cells are initiated, or the extent ofalready-identified storm cells increases or decreases, the differenceimage yields pixel values corresponding to the magnitude of the change.

[0055] In more detail, referring now to FIG. 5, the interest image isgenerated by first identifying large-scale image features such as thoseassociated with a line, or frontal storm. The large-scale image featurescan be identified using standard image-processing techniques, such aslow-pass spatial filtering of the received image. For example, alow-pass filter can be implemented by calculating an average at eachpixel of values of the surrounding pixels within a predetermined areaand replacing the value of the pixel with the value of the calculatedaverage. The process is repeated at each pixel in the image. Thepredetermined area can be identified by a “kernel” identifying the ofsurrounding pixels that will be averaged. The kernel can be any shape,such as a rectangle, an ellipse, a square, and a circle. Generally, somecare is required to select the size of the kernel, such that thelow-pass filter distinguishes image features considered large (e.g.,larger than a storm cell). In some embodiments, a scoring function isalso applied in combination with the kernel. For example, an averagevalue can be determined through application of the image kernel toaverage surrounding pixels within the kernel. The scoring functiongenerates an output value for the processed image based on the averagevalue. The scoring function can be used to de-emphasize lowaverage-value pixels and/or emphasize high average-value pixels.Generally, scoring functions are predetermined one-to-one mappings ofoutput pixel values for a range of input pixel values. Scoring functionscan be initially estimated and later refined based on empirical resultsto improve the overall forecast accuracy. The scoring functions can bedefined for any of the processed image features.

[0056] Determination of the interest image is also based on identifyingsmall-scale image features (Step 505). The small-scale image featuresare identified using standard image-processing techniques, such ashigh-pass spatial filtering of the received image. For example, ahigh-pass filter can be implemented by calculating for each pixel astandard deviation based on the pixel values of predeterminedsurrounding pixels and replacing the value of the subject pixel with thecalculated standard deviation value. The predetermined surroundingpixels can be identified using a kernel having a shape that can be thesame as the low-pass filter kernel. Alternatively, a kernel having adifferent shape can also be used as the low-pass filter. Care is alsorequired to select the size of the kernel, such that the high-passfilter distinguishes image features considered small (e.g., on the orderof a storm cell). As described above, a scoring function can be appliedto emphasize small-scale features and/or de-emphasize large-scalefeatures.

[0057] The interest image can also be further refined by identifyingother image details, such as edges, or structure (Step 510). In oneembodiment, peakiness indicative of image features having fine detail,such as those associated with cumulus formations are calculated. Thepeakiness image features are identified through standardimage-processing techniques, such as convolution filtering of thereceived image. For example, a convolution filter can be implemented bycalculating an autocorrelation at each pixel of values of thesurrounding pixels within a predetermined area and replacing the valueof the pixel with the value of the calculated autocorrelation. Care isalso required to select the size of the kernel, such that the peakinessfilter distinguishes image feature detail consistent with cumulusformation structure.

[0058] The interest image can optionally be based on classifying imagedetails into one of a number of predetermined weather categories. Someexamples of weather categories include lines, stratiform regions, largecells, and small cells. The image features can be classified throughstandard image-processing techniques, such as pattern recognition. Forexample, a number of different kernels can be used to process the imagein which each kernel is indicative of at least one of the stormclassifications being determined.

[0059] The interest image is generated from the received filtered imagesfrom implementation of the various spatial filters (Step 515). Theinterest image identifies areas of the received meteorological imagethat are likely to contain features indicative of a convective weatherevent.

[0060] Method of Generating the Short-Term Forecast

[0061] In more detail, referring now to FIG. 6, short-term convectiveweather forecast are generated by first identifying a first forecasttime (Step 600). Generally, the forecast time is selected as a timeranging from a several minutes to several hours. The forecast time, isgenerally measured from the time of the latest received meteorologicalimage.

[0062] A probability of convective weather of a predetermined category,or range of categories is generated at a first forecast time (Step 605).The generated probability of convective weather image is then advectedaccording to an advection field, to the first forecast time (step610)representing the forecast of convective weather. Image filtering can beapplied to the advected forecast image to smooth edges and fill in anydiscontinuities in the image (e.g., speckling, or holes). This laststage of image filtering is not driven by the forecast, but rather thephysical realities of the weather. The weather is not prone to abruptchanges in location, but rather exhibits some degree of smoothing. Insome embodiments, convective weather forecasts are generated at multiple“look ahead” times. For example, forecasts at 30 minutes, 60 minutes, 90minutes, and 120 minutes can be generated from the same received weatherimages. To accomplish this, a new forecast time is identified (step 615)and the process repeats from step 605. The results of previous forecastsderived from previously received weather images can be stored andcompared to the received weather images to determine the accuracy of theforecasts (i.e., scoring) (step 620).

[0063] In one example and with reference again to FIGS. 1 and 2, theshort-term predictor 102 generates a short-term storm forecastresponsive to receiving satellite meteorological images. First, theimage receive processor 202 receives a visible satellite imagerepresentative of albedo. The image is pre-processed by the receiveprocessor 202, for example, to remove pixels for which the albedo valueis below a predetermined threshold, such as 0.18, indicating a lack ofsignificant cloud formations. This preprocessing can simplify subsequentprocessing by removing or ignoring pixels that are not indicative ofconvective weather. The large-scale filter 206 receives the preprocessedimage and generates a visible large scale image by performing a spatial,or neighborhood averaging of the received image. In one embodiment, thelarge-scale filter uses a 15 pixel-by-15 pixel kernel. The large-scalefilter 206 centers the kernel on a pixel of the received (orreprocessed) meteorological image and averages all pixels of thereceived image within the boundaries of the kernel. The resultingaveraged value replaces the pixel value in the received image. Thekernel is subsequently moved to another pixel in the image, and thisprocess is repeated until averages have been computed for substantiallyall pixels.

[0064] Similarly, the small-scale filter 208 receives the pre-processedimage and generates a small-scale image by performing a spatial standarddeviation of the received image pixels. A 15 pixel-by-15 pixel kernel isused to determine the set of pixels for calculation of the standarddeviation. The peakiness filter 210 receives the pre-processed image andgenerates a peakiness image by filtering the image to accentuate cloudpeaks of the received image. In one embodiment, the peakiness image iscomputed by subtracting the large-scale image from the visible image.

[0065] The image filter processor 212 receives the large-scale image,the small scale image and the peakiness image and generates an interestimage. The interest image is generated by assigning an interest value topixels or regions of the processed images for which the standarddeviation is high and the large-scale filtered value is low. Thistypically includes cumulus existing in a region outside of an organizedstorm region. The resulting interest image is further processed to fillin holes, or gaps, and generally, to smooth the appearance of the image.The image filter processor 212 filters the image using an imageprocessing concepts of “dilate” and “erode.” In one embodiment, akernel, such as a 5 pixel-by-5 pixel kernel is applied to each pixel ofthe interest image. A dilate image is computed by replacing the value ofa pixel with the maximum pixel value of a group of pixels identified bythe kernel. The replacement process can be one or more times. Similarly,a kernel is applied to each pixel of the interest image and an erode isgenerated by replacing the value of the center pixel with the minimumvalue of the group of pixels identified by the kernel. The erode processcan be repeated a second time. The image filter processor 212 thentransmits the resulting interest image to the image growth-and-decayprocessor 214.

[0066] The growth-and-decay processor 214 also receives the differenceimage from the image difference processor 204 indicative of the growthand/or decay of cumulus elements. Cumulus elements exhibits a drop intemperature during the growth phase, as the cloud tops cool as theyincrease in altitude. The image growth-and-decay processor 214 thengenerates the growth/decay image by identifying weather severity levelsbased upon the received images. For all other image regions containingdata, the weather level is set to level 2. A zero value is assigned toall other regions of the image.

[0067] In a second example of operation of the short-term weatherpredictor 102, the image receive processor 202 receives a radar dataimage representative of precipitation (e.g., VIL). The image differenceprocessor 204 computes a precipitation difference image indicating areasof increasing and/or decreasing precipitation. The small-scale filter208 generates a small-scale image by taking the spatial standarddeviation of the received image. The image growth-and-decay processorremoves pixels from the difference image if the precipitation is below apredetermined level, or masks regions of the difference image for whichthe difference values are below a predetermined value.

[0068] By way of example and with reference to FIGS. 7A through 7D, theprocessing of a simplified, exemplary meteorological image is described.FIG. 7A represents a simplified first meteorological image, such as aradar image including a first weather element 702 indicative of aforecast parameter, such as VIL. The first weather element 702 is shownoptionally in relation to a graticule 700 (shown in phantom). Thegraticule 700 assists in identifying relative movement and location ofthe first weather element 702. FIG. 7B represents a simplified secondmeteorological image, such as a second radar image obtained from thesame radar as the first image, but at a later time. The weather imageincludes a later representation of the first weather element 702′. Acomparison of weather element 702′ to weather element 702 indicates thatthe weather element 702 has moved to a new location and increased insize (e.g., northeastward in this example, with north being representedby the twelve o'clock position of the graticule 700). The secondmeteorological image also includes additional weather elements 703, 704,705 appearing for the first time.

[0069] Referring now to FIG. 7C, a difference image is shownrepresenting the results of subtracting the advected firstmeteorological image illustrated in FIG. 7A, from the secondmeteorological image illustrated in FIG. 7B. A first difference weatherelement 702″ results from the increase in storm size. The new weatherelements 703, 704, 705 appear substantially unchanged because they werenot present in the first meteorological image. Applying the large-scalespatial filter to FIG. 7B will result in a large-scale image (not shown)that includes the first weather element 702′, but not the new weatherelements 703, 704, 705. Similarly, applying a small-scale, orstandard-deviation, filter to FIG. 7B results in a small-scale imageillustrated in FIG. 7D that includes the new weather elements 703, 704,705.

[0070] Having shown the preferred embodiments, one skilled in the artwill realize that many variations are possible within the scope andspirit of the claimed invention. It is therefor the intention to limitthe invention only by the scope of the claims.

What is claimed is:
 1. A method for a computer-assisted prediction ofnear-term development of convective meteorological events comprising thesteps: (a) determining a difference image by advecting a firstmeteorological image and combining the advected first meteorologicalimage and a second meteorological image, the first and secondmeteorological images each comprising data indicative of a firstforecast parameter at a first time and a second time, respectively; (b)generating an interest image comprising a region of interest byfiltering a third meteorological image; and (c) generating a growthimage indicative of the occurrence of a convective meteorological eventby combining the difference image and the interest image.
 2. The methodof claim 1 wherein the data of the first meteorological image comprise atwo-dimensional array of pixels, each of the of pixels having a valuequantifying the first forecast parameter for a predeterminedgeographical area.
 3. The method of claim 1 wherein the first forecastparameter comprises at least one of precipitation, infrared temperature,radar reflectivity, vertically-integrated liquid (VIL), temperaturestability, and albedo.
 4. The method of claim 1 wherein the step ofdetermining the difference image comprises subtracting the advectedfirst meteorological image from the second meteorological image.
 5. Themethod of claim 4 wherein the step of determining a difference imagecomprises the steps of: determining a plurality of preliminarydifference images; and averaging the plurality of preliminary differenceimages to generate the difference image.
 6. The method of claim 1wherein the third meteorological image is the first meteorologicalimage.
 7. The method of claim 1 wherein the third meteorological imageis indicative of a second forecast parameter.
 8. The method of claim 1wherein the step of generating the interest image comprises: (a)filtering the third meteorological image to generate alarge-scale-feature image; and (b) filtering the third meteorologicalimage to generate a small-scale-feature image.
 9. The method of claim 7wherein the step of filtering the large-scale-feature image compriseslow-pass filtering the third meteorological image.
 10. The method ofclaim 8 wherein the step of low-pass filtering comprisesneighborhood-average filtering.
 11. The method of claim 7 wherein thestep of filtering the small-scale-feature image comprises high-passfiltering the third meteorological image.
 12. The method of claim 10wherein the step of high-pass filtering comprisesneighborhood-standard-deviation filtering.
 13. The method of claim 1wherein the step of generating the interest image further comprisesfiltering the third meteorological image to generate a peakiness imageindicative of convective weather.
 14. The method of claim 12 wherein thestep of generating the peakiness image comprises the steps: (a)averaging the third meteorological image to generate an averagemeteorological image; and (b) subtracting the average meteorologicalimage from the third meteorological image.
 15. The method of claim 1further comprising the steps of: (a) combining the growth image and thefirst meteorological image to generate a forecast image identifying thelikelihood of convective meteorological events at a third time; and (b)advecting the combined image to the third time.
 16. The method of claim1 further comprising the step of advecting the growth image with respectto time.
 17. The method of claim 1 wherein the growth image comprises adecay image.
 18. The method of claim 1 further comprising the step ofclassifying weather elements of the first meteorological image.
 19. Themethod of claim 18 wherein the step of classifying weather elementscomprises selecting weather classifications from at least one of linestorm, stratiform, large cell and small cell.
 20. A method for acomputer-assisted prediction of near-term development of convectivemeteorological events comprising the steps: (a) determining a differenceimage by advecting a first precipitation image, and combining theadvected first precipitation image and a second precipitation image, thefirst and second precipitation images indicative of precipitation at afirst time and a second time, respectively; (b) generating an interestimage comprising a region of interest by filtering the secondprecipitation image; and (c) generating a growth image indicative of theoccurrence of a convective meteorological event by combining thedifference image with the interest image.
 21. The method of claim 20wherein step (a) comprises the steps of: determining a plurality ofpreliminary difference images; and averaging the plurality ofpreliminary difference images to generate the difference image.
 22. Themethod of claim 20 wherein the step of combining the advected firstprecipitation image and the second precipitation image comprisessubtracting the manipulated first precipitation image from the secondprecipitation image.
 23. The method of claim 20 wherein theprecipitation image comprises data representative of verticallyintegrated liquid water content.
 24. The method of claim 20 wherein thestep of generating the interest image comprises: (a) filtering the firstprecipitation image to generate a large-scale-feature image; and (b)filtering the first precipitation image to generate asmall-scale-feature image.
 25. The method of claim 24 wherein the stepof filtering the large-scale-feature image comprises low-pass filteringthe first precipitation image.
 26. The method of claim 24 wherein thestep of filtering the small-scale-feature image comprises high-passfiltering the first precipitation image.
 27. The method of claim 20further comprising the steps of: (a) generating a forecast imageidentifying the likelihood of convective meteorological events at athird time by combining the growth image and a current precipitationimage; and (b) advecting the combined image to the third time.
 28. Themethod of claim 20 further comprising the steps: (a) advecting a growthimage according to a first advection field; (b) advecting a currentprecipitation image according to a second advection field; and (c)combining the advected growth image and advected current precipitationimage to generate a forecast image.
 29. The method of claim 20 whereinthe growth image comprises a decay image.
 30. The method of claim 20further comprising the step of classifying weather elements of the firstmeteorological image.
 31. A method for a computer assisted prediction ofnear-term development of convective meteorological events comprising thesteps: (a) determining a difference image by advecting a first infraredmeteorological image and combining the advected first infrared image anda second infrared meteorological image, wherein the first and secondinfrared meteorological images are indicative of cloud temperature at afirst time and a second time, respectively; (b) generating an interestimage comprising a region of interest by filtering a satellite visiblemeteorological image; and (c) generating a growth image indicative ofthe occurrence of a convective meteorological event by combining thedifference image and the interest image.
 32. The method of claim 31wherein step (a) further comprises the step of: determining a pluralityof preliminary difference images; and averaging the plurality ofpreliminary difference images to generate the difference image.
 33. Themethod of claim 31 wherein the step of combining the advected firstinfrared meteorological image and the second infrared meteorologicalimage comprises subtracting the manipulated first infraredmeteorological image from the second infrared meteorological image. 34.The method of claim 31 wherein the step of generating the interest imagecomprises: (a) filtering the satellite visible meteorological image togenerate a large-scale-feature image; and (b) filtering the satellitevisible meteorological image to generate a small-scale-feature image.35. The method of claim 34 wherein the step of filtering thelarge-scale-feature image comprises low-pass filtering the satellitevisible meteorological image.
 36. The method of claim 34 wherein thestep of filtering the small-scale-feature image comprises high-passfiltering the satellite visible meteorological image.
 37. The method ofclaim 34 further comprising the step of filtering the satellite visiblemeteorological image to generate a peakiness image indicative of cumulusclouds.
 38. The method of claim 37 wherein the step of filtering thesatellite visible meteorological image comprises: (a) averaging thevisible satellite image to generate an average visible satellitemeteorological image; and (b) subtracting the average visible satelliteimage from the visible satellite meteorological image.
 39. The method ofclaim 31 further comprising the steps of: (a) generating a forecastimage identifying the likelihood of convective meteorological events ata third time by combining the growth image and a current precipitationimage; and (b) advecting the combined image to the third time.
 40. Themethod of claim 31 wherein the growth image comprises a decay image. 41.The method of claim 31 further comprising the step of classifyingweather elements of the first meteorological image.
 42. An apparatus forpredicting near-term development of convective meteorological eventscomprising: (a) means for advecting a first meteorological image andcombining the advected first meteorological image and a secondmeteorological image to generate a difference image, the first andsecond meteorological images indicative of a first forecast parameter ata first time and a second time, respectively; (b) filter means forgenerating an interest image comprising a region of interest byfiltering a third meteorological image; and (c) means for combining thedifference image and the interest image to generate a growth imageindicative of the occurrence of a convective meteorological event. 43.The apparatus of claim 42 wherein the filter means comprises: (a) alarge-scale feature detector means for filtering the thirdmeteorological image to generate a large-scale-feature image; and (b) asmall-scale feature detector means for filtering the thirdmeteorological image to generate a small-scale-feature image.
 44. Theapparatus of claim 43 wherein the large-scale feature detector compriseslow-pass filter means for generating a low-pass filtered rendition ofthe third meteorological image.
 45. The apparatus of claim 43 whereinthe small-scale feature detector comprises high-pass filter means forgenerating a high-pass filtered rendition of the third meteorologicalimage.
 46. The apparatus of claim 42 wherein the filter means furthercomprises a peakiness feature-detector means for generating a peakinessimage indicative of cumuliform features.
 47. The apparatus of claim 42wherein the growth image comprises a decay image.
 48. The apparatus ofclaim 43 further comprising a means for classifying weather elements ofthe first meteorological image.
 49. An apparatus for predicting thenear-term development of convective meteorological events comprising: animage receiver processor configured to receive a first and a secondmeteorological image; a difference processor in communication with theimage receiver processor, the difference processor determining adifference image in response to the first and second meteorologicalimages; an interest image processor in communication with the imagereceiver processor, the interest image processor determining an interestimage in response to the first meteorological image; a growth imageprocessor in communication with the difference processor and theinterest image processor, the growth image processor generating a growthimage in response to the difference image and the interest image; and aforecast processor in communication with the growth image processor andthe image receiver processor, the forecast processor determining ashort-term forecast in response to the first meteorological image andthe growth image.
 50. The apparatus of claim 49 wherein the interestimage processor comprises: a large-scale spatial filter; and asmall-scale spatial filter.
 51. The apparatus of claim 49 wherein theforecast processor receives weather-classification information from theinterest image processor and determines a short-term forecast inresponse to the first meteorological image, the growth image, and theweather-classification information.